基于AR模型的WSN流量多步预测算法研究

针对WSN流量预测,基于AR模型提出一种WSN流量双卡尔曼并行递推预测算法,该算法使用两个Kalman滤波器,交替进行AR模型参数的递推辨识与时变数据中真实值的最优估计,根据序列数据的最新信息实时修正AR模型参数进行动态预测。同时针对大步长的流量预测,引入滚动修正思想,克服动态预测算法存在间隔时间过长的缺点,降低多步预测误差。实验研究表明,利用研究的双卡尔曼并行递推算法使用AR模型进行多步预测,从原理设计和实现算法上,实现了WSN流量的准确预测。     

Real-time localization estimator of mobile node in wireless sensor networks based on extended Kalman filter

Localization of the sensor nodes is a key supporting technology in wireless sensor networks (WSNs). In this paper, a real-time localization estimator of mobile node in WSNs based on extended Kalman filter (KF) is proposed. Mobile node movement model is analyzed and online sequential iterative method is used to compute location result. The detailed steps of mobile sensor node self-localization adopting extended Kalman filter (EKF) is designed. The simulation results show that the accuracy of the localization estimator scheme designed is better than those of maximum likelihood estimation (MLE) and traditional KF algorithm.     

基于扩展卡尔曼滤波器的网络队列预测

为解决网络队列动态预测问题,提出一个网络系统在泊松分布流量和指数服务时间下的暂态队列行为预测模型并进行仿真验证。阐述基于扩展卡尔曼滤波（Kalman）的预测模型及其具体算法,结合网络中的数据流量特性,构建基于扩展卡尔曼滤波器的网络暂态队列预测模型,并根据仿真网络中的实际数据对模型进行验证。实验结果表明,所建立的网络暂态队列实时预测模型预测效果比较理想,基本与实时队列长度保持一致。因此,该模型可以较低的代价应用于网络中的动态路由算法及拥塞控制算法中。     

基于模式匹配的行程时间预测模型研究

本文采用深圳市某快速路的占有率预测目标的历史数据库对交通状态进行模式匹配和行程时间进行预测。经过比较研究得出,利用占有率数据进行模式匹配预测城市快速路的行程时间,结果是最令人满意的。这一预测模型的构建为智能运输的应用建立了良好的理论基础。     

基于视频的城市快速路交通流检测及车辆行为监控

提出一种针对城市快速路的视频监控系统,完成交通流及道路异常行为检测.流量检测模块对车辆进行实时跟踪获得各类交通流统计数据,实现快速路车辆运行状况的全面掌控.异常监控模块运用隐马尔科夫模型,对带有时间和空间信息的车辆轨迹进行训练,获得路径划分后对道路车辆轨迹进行参数匹配,提取诸如超低(高)速行驶、违章停车、违规掉头等异常...     

山区高速公路隧道与互通立交出口间距

针对单向双车道高速公路隧道与互通立交出口间距只能满足识别视距的情况,分析了车辆驶出隧道出口的换道过程,结果表明,在极端困难情况下,隧道出口的明适应时间能够缩短到1s?预期驶出高速的车辆必须在隧道前的路段完成变道,确保驶出车辆在车道外侧上行驶,才可以保证运营期交通行车安全。     

基于EKF和RBF的路面附着系数估计

针对车辆行驶下的路面附着系数估计问题,提出了扩展卡尔曼滤波算法(EKF,Extended Kalman Filter)与径向基神经网络(RBF,Radial Basis Functionneural network)相融合。通过扩展卡尔曼滤波算法得出路面附着系数估计所需要的车辆状态参数,结合轮速等直接数据采用径向基神经网络对路面附着系数进行估计。神经网络的训练样本通过Carsim/Simulink收集不同行驶工况,并采用差值寻优的方法对径向基神经网络算法中的决定系数进行优化。基于双移线工况验证了该算法在路面附着系数估计上具有较高的精准度。     

用于交通运营管理的实时交通流状态分类高斯混合模型

采用高斯混合模型GMM,同时以交通流量、平均速度和密度3种交通流宏观特征为指标,对交通流状态进行聚类和分类.和其他聚类分类方法比较,高斯混合模型是结构化的模型,适合于各种情形交通流参数.高斯混合模型中子类的个数通过Gap统计量结合交通流的领域知识加以确定,而模型的其他参数则由E-M算法进行估计.所建立的GMM模型可以作...     

灰色预测理论在城镇居民可支配收入预测中的应用

通过综合适用于短期预测的GM（1,1）模型和适用于随机波动较大的数列预测的马尔科夫模型GMM（1,1）,对2000年-2006年十堰市城镇居民可支配收入进行建模计算预测,结果表明模型的预测精度较高。为制定新一轮的经济政策提供科学的决策依据。     

A new approach for target motion analysis with signal time delay

A new approach is proposed in this paper for the problem of the target motion analysis (TMA) with signal propagation time delay. This problem is an unobservable tracking problem in which the acoustic signal transmits with time delay. We present an intelligent range parameterized unscented Kalman filter (IRPUKF) algorithm to estimate the state of the nonlinear unobservable tracking system and propose a recursive model parameter online adjustment method to deal with the time delay in signal propagation. In a simulation of tracking target using a maneuvering acoustic sensor with signal time delay case study, the effectiveness and efficiency of the proposed algorithm is testified to perform better, compared with the range parameterized extended Kalman filter (RPEKF) algorithm.     

一种新的MPEG VBR流量带宽分配算法

本文针对MPEG VBR多媒体流量的特征,结合小波和卡尔曼滤波的特点,在新的网络流量预测模型的基础上,提出了一种新的多媒体流自适应带宽分配算法,并在NS仿真平台中实现该算法。结果表明,该算法能够有效避免网络拥塞,降低网络传输时延,减少数据丢包率,明显地提高了带宽利用率,具有较好的实时性,支持QOS。     

Car-following theory of steady-state traffic flow using time-to-collision

The conventional car-following theory is based on the assumption that vehicles will travel along the center line of lanes. However, according to the field survey data, in complex traffic conditions, a lateral separation between the leader and the follower frequently occurs. Accordingly, by taking lateral separation into account, we redefined the equation of time-to-collision (TTC) using visual angle information. Based on the stimulus-response framework, TTC was introduced into the basic General Motors (GM) model as a stimulus, and a non-lane-based car-following model of steady-state traffic flow was developed. The property of flow-density relationship was further investigated after integrating the proposed car-following model with different parameters. The results imply that the property of steady-state traffic flow and the capacity of each lane are highly relevant to the microscopic staggered car-following behavior, and the proposed model significantly enhances the practicality of the human driving behavior model.     

基于UKF算法的电池荷电状态SOC估算研究

新能源汽车锂电池荷电状态是反映电池及电源系统的重要参数,为达到实时估算SOC目的,基于无迹卡尔曼滤波算法提出SOC估算解决方案。在MATLAB/Simulink环境中建立一阶Thevenin等效电路模型和无迹卡尔曼滤波算法,通过建立混合功率脉冲特性实验,辨析出不同SOC和温度对电池模型的影响参数,将辨析出来的参数代入到UKF算法中进行仿真实验。实验结果表明,该荷电状态估算具有较高的精准度。     

一种智能预测方法在电梯群控系统交通分析中的应用

交通流预测是电梯群控系统的重要组成部分 .将基于神经网络的时间序列预测理论应用到电梯群控系统的交通分析中 ,构造了一种神经网络时间序列交通流预测模型 .仿真实验表明 ,这种交通流智能预测方法是有效的     

ADAPTIVE ESTIMATION OF THE MEANS AND COVARIANCES OF SYSTEM NOISES

This paper presents a simple and feasible method of estimating the means andcovariances of system noises.The information about the means and covariances is derivedfrom a suboptimal Kalman filter formed via the approximate statistics.An efficientalgorithm is obtained through WLS(weighted-least-square)estimation by using themeasurement residuals of the suboptimal Kalman filter.Simulations are given to show theefficiency of the method.     

Use of superposition principle to derive a general mathematical model to simulate one-to-one, one-to-multi and multi-to-multi saw filter designs

This paper explains and summarizes a new attempt to derive a general mathematical model [GMM] to simulate surface acoustic wave (SAW) filters, using the superposition principle and delta function model. GMM can be used to simulate One-to-One, One-to-Multi and Multi-to-Multi SAW filter devices. The simulation program was written using MATLAB (the language of technical computing). Four-design structures (One-to-One, One-to-Two, One-to-Three and Ten-to-Ten) ware selected to test the correctness of GMM. The frequency response of the simulation and test results are similar in center frequency and 3-dB bandwidth, but the insertion loss is different, because of some second order effects (Issa Haitham, 1999).     

基于状态空间神经网络和扩展卡尔曼滤波的主动交通感应控制

为改善方案选择式交通感应控制输出的交通信号配时方案滞后于实时交通状态的缺点,提出用状态空间神经网络和扩展卡尔曼滤波模型预测未来交通状态的优化配时方案.采用能反映道路网络几何特征的状态空间神经网络拓扑结构,结合当前时段和前一时段的路段交通状态,预测下一时段交通状况并选择与其相匹配的信号配时方案;应用扩展卡尔曼滤波训练状态空间神经网络,提高其训练效率及精度.选用南京市广州路的实测交通数据和由多目标遗传算法得出的最优信号控制方案验证模型的有效性.研究结果表明,与BP神经网络和状态空间神经网络相比,所提出的模型能够根据道路状况选择合适的交通控制方案.     

Magnetometer-only linear attitude estimation for bias momentum pico-satellite

Satellite attitude information is essential for pico-satellite applications requiring light-weight, low-power, and fast-computation characteristics. The objective of this study is to provide a magnetometer-only attitude estimation method for a low-altitude Earth orbit, bias momentum pico-satellite. Based on two assumptions, the spacecraft spherical symmetry and damping of body rates, a linear kinematics model of a bias momentum satellite＇s pitch axis is derived, and the linear estimation algorithm is developed. The algorithm combines the linear Kalman filter （KF） with the classic three-axis attitude determination method （TRIAD）. KF is used to estimate satellite＇s pitch axis orientation, while TRIAD is used to obtain information concerning the satellite＇s three-axis attitude. Simulation tests confirmed that the algorithm is suited to the time-varying model errors resulting from both assumptions. The estimate result keeps tracking satellite attitude motion during all damping, stable, and free rotating control stages. Compared with nonlinear algorithms, such as extended Kalman filer （EKF） and square root unscented Kalman filer （SRUKF）, the algorithm presented here has an almost equal performance in terms of convergence time and estimation accuracy, while the consumption of computing resources is much lower.     

Particle filter for joint frequency offset and channel estimation in MIMO-OFDM systems

A particle filter is proposed to perform joint estimation of the carrier frequency offset (CFO) and the channel in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless communication systems. It marginalizes out the channel parameters from the sampling space in sequential importance sampling (SIS), and propagates them with the Kalman filter. Then the importance weights of the CFO particles are evaluated according to the imaginary part of the error between measurement and estimation. The varieties of particles are maintained by sequential importance resampling (SIR). Simulation results demonstrate this algorithm can estimate the CFO and the channel parameters with high accuracy. At the same time, some robustness is kept when the channel model has small variations.     

基于TDOA/Doppler测量的联邦UKF移动位置估计算法(英文)

为了进一步提高移动台的跟踪和定位性能,提出了一种基于联邦滤波结构和简化UKF的移动位置最优估计与融合新方法.该算法以Singer移动台运动模型作为参考系统,以简化UKF滤波器作为子滤波器,对2组独立检测的TDOA和Doppler测量值进行局部估计;然后在主滤波器中,对子滤波器的估计结果按标量加权进行最优融合,得到全局最优或次最优融合估计结果;最后主滤波器利用全局估计结果对子滤波器和参考系统进行反馈和信息重置,以进行下一步估计.仿真试验中,对该算法用于移动台位置估计的效果和性能进行评估,并与基于TDOA和基于Doppler的简化UKF方法做比较.仿真结果表明,该算法能有效降低移动台位置估计的误差和方差,具有良好的均方根误差和均值误差CDF性能.